WATER RESOURCES RESEARCH, VOL. 48, W11512, doi:10.1029/2011WR010482, 2012
Surface energy balance and actual evapotranspiration of the
transboundary Indus Basin estimated from satellite measurements
and the ETLook model
W. G. M. Bastiaanssen,1,2 M. J. M. Cheema,1,3,4 W. W. Immerzeel,5,6 I. J. Miltenburg,2
and H. Pelgrum2
Received 26 January 2011; revised 3 September 2012; accepted 19 September 2012; published 21 November 2012.
[1] The surface energy fluxes and related evapotranspiration processes across the Indus
Basin were estimated for the hydrological year 2007 using satellite measurements. The new
ETLook remote sensing model (version 1) infers information on actual Evaporation (E) and
actual Transpiration (T) from combined optical and passive microwave sensors, which can
observe the land-surface even under persistent overcast conditions. A two-layer
Penman–Monteith equation was applied for quantifying soil and canopy evaporation. The
novelty of the paper is the computation of E and T across a vast area (116.2 million ha) by
using public domain microwave data that can be applied under all weather conditions, and
for which no advanced input data are required. The average net radiation for the basin was
estimated as being 112 Wm2. The basin average sensible, latent and soil heat fluxes were
estimated to be 80, 32, and 0 Wm2, respectively. The average evapotranspiration (ET) and
evaporative fraction were 1.2 mm d1 and 0.28, respectively. The basin wide ET was 496 6
16.8 km3 yr1. Monte Carlo analysis have indicated 3.4% error at 95% confidence interval for
a dominant land use class. Results compared well with previously conducted soil moisture,
lysimeter and Bowen ratio measurements at field scale (R2 ¼ 0.70; RMSE ¼ 0.45 mm d1 ;
RE ¼ –11.5% for annual ET). ET results were also compared against earlier remote sensing and
modeling studies for various regions and provinces in Pakistan (R2 ¼ 0.76; RMSE ¼ 0.29
mmd1 ; RE ¼ 6.5% for annual ET). The water balance for all irrigated areas together as one
total system in Pakistan and India (26.02 million ha) show a total ET value that is congruent
with the ET value from the ETLook surface energy balance computations. An unpublished
validation of the same ETLook model for 23 jurisdictional areas covering the entire Australian
continent showed satisfactory results given the quality of the watershed data and the diverging
physiographic and climatic conditions (R2 ¼ 0.70; RMSE ¼ 0.31 mmd1 ; RE ¼ –2.8%
for annual ET). Eight day values of latent heat fluxes in Heibei (China) showed a good
resemblance (R2 ¼ 0.92; RMSE ¼ 0.04 mm d1 ; RE ¼ 9.5% for annual ET). It is
concluded that ETLook is a novel model that can be operationalized further—especially after
improving the preprocessing of spaceborne soil moisture data. This preprocessing includes
(1) downscaling of topsoil moisture from 25 to 1 km pixels, and (2) translation of topsoil
moisture into subsoil moisture values.
Citation: Bastiaanssen, W. G. M., M. J. M. Cheema, W. W. Immerzeel, I. J. Miltenburg, and H. Pelgrum (2012), Surface energy
balance and actual evapotranspiration of the transboundary Indus Basin estimated from satellite measurements and the ETLook model,
Water Resour. Res., 48, W11512, doi:10.1029/2011WR010482.
1
Water Management Department, Faculty of Civil Engineering and
Geosciences, Delft University of Technology, Delft, Netherlands.
2
eLEAF Competence Center, Wageningen, Netherlands.
3
Department of Irrigation and Drainage, University of Agriculture,
Faisalabad, Pakistan.
4
International Water Management Institute, Lahore, Pakistan.
5
FutureWater, Wageningen, Netherlands.
6
Department of Physical Geography, University of Utrecht, Utrecht,
Netherlands.
Corresponding author: M. J. M. Cheema, Department of Irrigation and
Drainage, University of Agriculture, Faisalabad, Pakistan. (mjm.cheema@
gmail.com)
©2012. American Geophysical Union. All Rights Reserved.
0043-1397/12/2011WR010482
1.
Introduction
[2] Planning and monitoring of consumptive water use is
necessary for sound management of scarce water resources.
Consumptive use influences social, economic, agricultural,
and environmental development. Water is consumed mainly
through evaporation (E) and transpiration (T) (jointly termed
evapotranspiration (ET)) from crops, soil, forests, urban
areas, and natural vegetation, among others. If precipitation
over a specific land cover exceeds ET (e.g., forests), such a
land cover class is a net producer of water resources. Nonconsumed water from precipitation feeds streams, rivers and
aquifers. If, however, ET exceeds precipitation, such a land
cover class will be a net consumer of water resources.
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Irrigated lands are a typical example of a net consumer of
water. ET information can be used for irrigation management [Allen et al., 2007; Bastiaanssen et al., 1996],
drought detection [e.g., Calcagno et al., 2007], real water
savings [e.g., Seckler, 1996], water accounting [e.g.,
Molden and Sakthivadivel, 1999], water productivity [e.g.,
Zwart et al., 2010], virtual water trade [e.g., de Fraiture
and Wichelns, 2010], model calibration [e.g., Immerzeel
and Droogers, 2008], hydrological model applications
[Droogers et al., 2010] and groundwater management
[Ahmad et al., 2005].
[3] A number of techniques are in use to measure ET,
ranging from conventional point measurements to modeling and spatially distributed remote sensing estimates. At
individual plant and field scales, lysimeters, heat pulse velocity, Bowen ratio, scintillometry, surface renewal, and
eddy correlation are commonly used [e.g., Meijninger
et al., 2002; Nagler et al., 2005]. Field scale ET measurements are generally considered accurate, however the accuracy of these traditional methods is often less than 90%
[Teixeira and Bastiaanssen, 2012; Twine et al., 2000]. The
equipment cost, extensive labor, and coverage issues
restrict use of these techniques at large scale [Elhaddad
and Garcia, 2008]. At the regional scale, earth observations
by means of satellite data are gradually becoming more
accepted [e.g., Anderson et al., 2007; Courault et al.,
2005; Guerschman et al., 2009; Kalma et al., 2008; Mu
et al., 2007; Wu et al., 2012] although operational data provision remains rare. This paper aims at contributing to the
development of operational systems that could be applied
on a daily time step for areas with limited ground data.
Routine weather data is assumed to be available.
[4] Evapotranspiration computations are often based on
surface energy balances [e.g., Long and Singh, 2012; Mu
et al., 2007; Price, 1990; Senay et al., 2007; Tang et al.,
2009]. Many of these energy balance models require thermal infrared radiation from cloud free images and atmospheric corrections in order to produce accurate land surface
temperature maps [Jia et al., 2009]. Cloud free surface temperature images for large areas in basins with monsoon climates are not straightforward to obtain [e.g., Bastiaanssen
and Bandara, 2001]. Thermal infrared radiation is more
sensitive to atmospheric water vapor absorption than visible
and near-infrared radiation [Lillesand and Kiefer, 2000],
and it is thus more challenging to acquire land surface temperature maps not being thwarted by clouds. For instance,
the surface temperature product (MOD 11A2) available
through Moderate Resolution Imaging Spectro radiometer
(MODIS) is thwarted by cloud cover for the entire period of
monsoon 2007 (June–September). About 50% of the basin
area was found without or with limited surface temperature
data from day of year (DOY) 161 to 241. This illustrates the
difficulty in getting continuous information for ET computations in irrigated areas from thermal infrared data. While
it is generally accepted that thermal infrared data provide
reliable results based on sound physics [e.g., Allen et al.,
2011; Allen et al., 2010; Bastiaanssen et al., 2008], the
cloud cover is a serious hindrance to routine applications in
various parts of the world.
[5] To circumvent these problems, the current study
deployed the first version of the ETLook algorithm. Soil
moisture derived from passive microwave sensors is the
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driving force for calculation of the surface energy balance
in ETLook. Surface soil moisture relates typically to a
depth of 2 to 3 cm, and the number of surface soil moisture
databases is growing due to an increasing number of operational passive microwave sensors. The accuracy and spatial
resolution are expected to improve in the near future.
Future soil moisture data layers will be based on active
Synthetic Active Radar (SAR) measurements, once this
data become available easily and free of charge. This is a
good moment to explore and develop ET models that are
based on soil moisture data sets.
[6] Microwave radiometry is less affected by cloud
cover [Fily et al., 1995; Ulaby et al., 1981] and can thus
provide continuous surface soil moisture information even
in monsoon periods. Li et al. [2006] have shown the value
of using microwave derived near-surface soil moisture in a
two-source energy balance model over an agricultural area
in central Iowa (USA). The ETLook algorithm is a twosource model and surface soil moisture is used for the computation of E, and a parameterization is introduced to compute
subsoil moisture content for the determination of T.
[7] Accurate ET information is of paramount importance
for the 116.2 million hectares (mha) Indus Basin, with high
elevation water source areas, a distinct monsoon climate
with cloud covered regions, and declining water tables due
to over-exploitation. This study was a first attempt to use
microwave technologies to accurately estimate ET over the
Indus Basin, and to detect areas with excessively high ET
rates using a spatial resolution of 1 km. Such a resolution is
thought to be good enough for regional-scale applications.
The main objective of this study was to demonstrate the validity of a combined optical and microwave based energy
balance model (ETLook) in a vast river basin with large
irrigation systems. Another objective was to use public domain data to estimate ET in the areas where field data are
not available, and to show water managers that spatially
discrete ET information is the basis for describing the
major water flowpath in ungauged basins.
2.
Study Area
[8] The study area is the Indus Basin, which lies between
latitude 24 380 to 37 030 N and longitude 66 180 to 82 280 E.
The total area of the basin is 116.2 mha and encompasses
four countries (Pakistan : 53%, India: 33%, China: 8% and
Afghanistan: 6%) (Figure 1). The basin exhibits complex
hydrological processes due to variability in topography,
rainfall, and land use. The elevations range from 0–8611 m
above mean sea level (a.m.s.l) and mean annual rainfall
varies between approximately 200 to 1500 mm. The basinwide average rainfall for 2007 was 383 mm yr1 [Cheema
and Bastiaanssen, 2012]. The basin has two distinct agricultural seasons, being the wet kharif monsoon season
(May to October) and the dry rabi season (November to
April). Wheat is the major rabi crop while rice and cotton
are major kharif crops. Irrigated agriculture is practiced in
26.02 mha (22.6% of total basin area) area of the basin (see
Figure 1). The irrigation system in the Indus Basin supplies
surface water to the middle and lower parts. The era of tubewell installations with subsidized rates and direct access to
water has motivated farmers to augment shortages in surface water with groundwater resources [Shah et al., 2000].
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Figure 1. Location of the Indus Basin and provinces of different countries in the basin. PK stands for
Pakistan and IN for India. The irrigated areas in the basin are also shown. [from Cheema and Bastiaanssen,
2010].
Currently 40–50% of agricultural water needs are met
through groundwater used in conjunction with surface water
[Sarwar and Eggers, 2006].
3.
Material and Methods
3.1. Satellite Data and Preprocessing
[9] Key input data for ETLook are: surface soil moisture, spectral vegetation index, surface albedo, atmospheric
optical depth, land use and land cover (LULC), soil physical properties, and meteorological data. Surface soil moisture was obtained from the Advanced Microwave Scanning
Radiometer (AMSR-E) on the Aqua satellite. Daily soil
moisture datasets with 25 km foot print (ascending and descending path) covering the Indus Basin were downloaded
from the National Snow and Ice Data Center (NSIDC) website (see http://nsidc.org/data/ae_land3.html) for the complete year of 2007 [Njoku, 2008]. The year 2007 was
selected because all required auxiliary data were available.
The actual spatial resolution of C-band AMSR-E soil moisture is large (approximately 70 km 40 km). AMSR-E
collects 60 km resolution C-band brightness temperature
with a sampling interval of 10 km, which allows AMSR-E
C-band data to be gridded at 25 km resolution. The operational character of surface soil moisture in NSIDC contributes to the construction of a routine provision of spatial ET
data bases. A comprehensive soil moisture data validation
study in the Indus Basin was performed by Cheema et al.
[2011]. The soil moisture data was validated against rainfall, vegetation and saturated water content. The soil moisture has shown strong relationship with rainfall and
vegetation. It was found that both the behavior as well as
the absolute values of topsoil moisture are realistic and provide sufficient information on the spatial and temporal
changes of topsoil moisture in the Indus Basin. The daily
layers were in the current study averaged to obtain 8-day
soil moisture layers to be compatible with the MODIS optical satellite data.
[10] This Indus Basin ETLook study required topsoil
moisture at 1 km scale, while the data is provided at 25 km
scale. Various sophisticated methods are documented in the
literature to downscale the available coarse resolution soil
moisture data to 1 km pixels [Friesen et al., 2008; Gharari
et al., 2011; Hemakumara et al., 2004; Merlin et al., 2006,
2008]. All these downscaling methods require a number of
parameters and have an empirical character related to the
physiographical setting of a specific area. More research
studies are required to find more generic solutions to this
problem, and it is outside the scope of this paper to compare and validate all these methods. Due to the absence of
detailed soil moisture data in the Indus Basin, a simple
method of downscaling based on effective saturation has
been adopted in this study. Each AMSR-E pixel was downscaled to 1 km using a bilinear resampling technique first.
This is simplistic, but is necessity to remove abrupt changes
in the data layers due to the texture of the large-scale
AMSR-E pixels. The information on saturated and residual
moisture content (sat and res, respectively) for each soil
type
top was used to calculate topsoil effective saturation
Se;xy at 1 km grid using the definition proposed by van
Genuchten [1980] as:
top
Se;xy
¼
AMSRE res;xy
;
sat;xy res;xy
(1)
top
where Se;xy
, AMSRE, sat,xy, and res,xy represent the effective
saturation, AMSRE soil moisture, saturated and residual
moisture content at 1 km pixel (x,y), respectively. The values
for sat,xy and res,xy were inferred from the Food and Agriculture Organization (FAO) soil map [FAO, 1995] using
pedo-transfer functions (P. Droogers, unpublished data,
2006). The minimum and maximum values of a particular
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pixel or geographic area has not been used because it cannot
be certified that these very extreme end points do ever occur.
Soils with a large pore volume (sat) contain more air and
have a lower degree of saturation. Their drier conditions
reduce soil evaporation because soil moisture is retained
stronger to the soil matrix, and the volume with water filled
pores that are needed to transport water will be lower under
dry conditions. While being simplistic, a scaling with sat
has certain merits.
[11] In addition, the saturation of the subsoil Sesub is
required for the computation of root water uptake and subsequent crop transpiration. The preprocessing of saturation
of the subsoil was done by using an empirical relationship
between Setop , the vegetation photosynthetical activity (that
reflect soil water availability with a certain lag time) and
Sesub . The following relationship is imbedded in ETLook1.0
and was applied in the current study:
Sesub ¼ 0:1LAI þ ð1 0:1LAIÞ½1 exp fSetop ð0:5LAI 1Þg;
(2)
where LAI is the Leaf Area Index. The basic assumption is
that the degree of saturation of the subsoil exceeds the saturation of the topsoil when vegetation is photosynthetically
active, and that Setop affects the level of Sesub under all conditions. The green LAI reflects the access of vegetation to soil
water across a longer period. Since it is based on spectral
leaf reflectances, LAI represents the real state conditions of
the canopy, including its leaf water content, among others.
The LAI does not reflect the daily moisture conditions of the
canopy and the subsoil, the day-to-day variability of Sesub is
therefore entirely regulated by Setop . In absence of green
plants, moisture in the subsoil holds a direct analytical relationship with the moisture in the topsoil [e.g., Hillel, 1998].
Hence, passive microwave data in combination with LAI
describes the daily variation of root zone soil water content.
[12] The Normalized Difference Vegetation Index (NDVI)
is an undisputed indicator of active vegetation and was used
to compute LAI as explained further down. It has been demonstrated by, for instance Nagler et al. [2005] and Burke
et al. [2001], that NDVI is an indicator of ET fluxes, which
is in line with equation (2). NDVI data are distributed by the
Land Processes Distributed Active Archive Center (LP
DAAC), located at the U.S. Geological Survey (USGS) Earth
Resources Observation and Science (EROS) Center (see
lpdaac.usgs.gov). Two 16-day NDVI datasets (MOD13A2
and MYD13A2 (collection 5) starting from day 1 and day 9,
respectively) at 1 km were used to create 8-day NDVI layers.
The vegetation cover (VC) was derived from NDVI following Jiang et al. [2006] as:
VC ¼ 1 NDVIfv NDVI
NDVIfv NDVIbs
0:7
:
(3)
[13] Threshold values of NDVI ¼ 0.8 and 0.125 were
used as boundary condition for full vegetation cover
(NDVIfv) and bare soil (NDVIbs), respectively. The LAI
was computed from NDVI values using standard asymptotic relationships between LAI and VC [e.g., Carlson and
Ripley, 1997; Curran and Steven, 1983]:
LAI ¼ ln½ð1 VCÞ=a;
(4)
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where a is the light extinction coefficient with a value
range of 0.40 to 0.65. An average value of 0.5 was taken
for all representative vegetation types [e.g., Kale et al.,
2005]. The LAI (VC) relationship was similar for all land
use classes because a values for all classes were not available and we assumed that a differences between classes
were small enough to justify the use of a few selective a
values, for all classes.
[14] Surface albedo was also derived from standard
MODIS products. The 8-day albedo data product MCD43B3
(collection 5) at 1 km resolution was downloaded from (see
https://wist.echo.nasa.gov/wist/api/imswelcome/) server provided by LP DAAC.
[15] Solar radiation is classically computed from the
extraterrestrial radiation in association with an atmospheric
transmissivity in the solar spectrum. The atmospheric transmissivity of shortwave radiation can be inferred from optical depth information provided by the MODIS cloud
product [King et al., 1997]. One km resolution MYD06_L2
values of the optical depth product were downloaded from
https://wist.echo.nasa.gov/wist/api/imswelcome/ to estimate
atmospheric transmissivity for the Indus Basin. The cloud optical depth from the MODIS products was used to infer
atmospheric transmissivity of shortwave radiation MODIS
[Barnard and Long, 2004].
[16] A detailed Land Use and Land Cover (LULC) map
of the Indus Basin developed by Cheema and Bastiaanssen
[2010] was used to infer information on different LULC
classes in the basin. Twenty-seven LULC classes were
identified. This LULC classification was used to create
look-up tables for the definition of certain bio-physical parameters required for ET computations, such as minimum
stomatal resistance, moisture sensitivity and maximum obstacle height.
[17] Rainfall (R) data are used to determine interception
evaporation. Interception (I) is computed on a daily scale
with the classical von Hoyningen model following von
Hoyningen-Hune [1983] and Braden [1985].
"
I ¼ 0:2LAI 1 1
ðVCÞR
1 þ 0:2LAI
!#
;
(5)
which assumes that maximum a water film of 0.2 mm is
stored per unit LAI. This coefficient can be modified. ET
cannot exceed R without being augmented by additional
water resources. Rainfall is therefore a good measure to
validate ET of natural vegetation against. By absence of
sufficient rain gauges, rainfall was obtained at spatial resolution of 25 km using Tropical Rainfall Measuring Mission
(TRMM) processing algorithms described by Huffman et al.
[2007]. The global rainfall algorithm (3B43 V6) available
through NASA website (see http://neo.sci.gsfc.nasa.gov/
Search.html?group¼39) was used. It provides monthly accumulated rainfall data, which has been calibrated and validated according to the Geographical Differential Analysis
(GDA) as outlined in Cheema and Bastiaanssen [2012].
3.2. Meteorological Data
[18] The major portion of the Indus Basin (53%) lies
within the administrative boundaries of Pakistan. Most of the
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meteorological data (e.g., air temperature, relative humidity
and wind speed) were therefore obtained from 65 meteorological stations under the aegis of the Pakistan Meteorological Department (PMD). Weather station data for India,
China and Afghanistan were extracted from the National
Oceanic and Atmospheric Administration (NOAA) National
Climatic Data Center (NCDC). The NCDC collects meteorological data from real time reporting stations worldwide in
agreement with World Meteorological Organization regulations (ftp://ftp.ncdc.noaa.gov/pub/data/gsod/). Data from 16
stations with complete datasets were downloaded. Hence, air
temperature, relative humidity and wind speed data from 81
stations collected at standard height of 2 m were obtained.
[19] ETLook requires gridded meteorological data for
air temperature (Tair), relative humidity (RH) and wind
speed (U2) at 1 km resolution. Topography, land use, sun
angle, and distance from water bodies directly affects the
spatial variability of near surface meteorological parameters [Brutsaert, 1982; Schulze et al., 1993]. Ordinary geospatial interpolation techniques do not take these variables
into account. The meteorological distribution model (Daymet) described by Thornton et al. [1997] was therefore
used to convert point data to spatial meteorological data.
Daymet uses a truncated Gaussian weighting filter for
regional distribution of climatic variables in relation with topography. A 1 km Digital Elevation Model (DEM) obtained
from GTOPO30 database (see http://eros.usgs.gov/#/Find_
Data/Products_and_Data_Available/gtopo30_info) was used
to establish relationships between the climatic variables and
topography.
[20] The weather grids for 2007 were independently validated against values from the International Water Management Institute (IWMI) world water and climate atlas that is
based on long-term field measurements and specific spatial
interpolation procedures (http://www.iwmi.cgiar.org/WAtlas/Default.aspx). The atlas provides monthly summaries of
rainfall, temperature, humidity, wind speed, and sun shine
hours at 18 km grid averaged over the period 1961–1990, as
produced by the University of East Anglia [New et al.,
1999]. The 8-day Daymet estimates were aggregated to
monthly values in order to make them comparable with the
IWMI atlas. A high coefficient of determination (R2 >
0.85) was obtained for air temperature estimates. However,
for relative humidity and wind speed, moderate coefficients
of determination (R2 ¼ 0.70–0.80 and 0.60–0.70, respectively) were achieved. These correlations are considered reasonable because IWMI atlas values are monthly averages for
1961–1990, which may be different for different years. The
IWMI atlas values are also interpolated and extrapolated,
and associated with a certain uncertainty. Nevertheless, the
impression is that air humidity and wind speed values display more uncertainty than air temperature.
3.3. Theoretical Background of ETlook
[21] The surface energy balance can be written as:
Rn G ¼ E þ H
ðWm2 Þ;
(6)
where Rn is net radiation, G is soil heat flux, E is latent
heat flux and H is the sensible heat flux. E is associated
with ET. The ETLook algorithm uses a two layer approach
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to solve the Penman–Monteith equation. The Penman–
Monteith equation for E and T can be written as:
e
ðRn;soil GÞ þ cp ra;soil
E¼
;
soil
þ 1 þ rra;soil
(7)
e
ðRn;canopy Þ þ cp ra;canopy
;
T¼
rcanopy
þ 1 þ ra;canopy
(8)
where E and T are evaporation and transpiration, respectively, in Wm2 ; (mbar K1) is the slope of the saturation vapor pressure curve, which is a function of air
temperature (Tair, C) and saturation vapor pressure (es,
mbar); e(mbar) is vapor pressure deficit, which is the difference between the saturation vapor content and the actual
vapor content; (kg m3) is the air density, and cp is specific heat of dry air ¼ 1004 J kg1 K1 ; (mbar K1) is
the psychometric constant; Rn,soil and Rn,canopy are the net
radiations at soil and canopy, respectively ; rsoil and rcanopy
are resistances of soil and canopy, while ra,soil and ra,canopy
are aerodynamic resistances for soil and canopy, respectively.
All resistances are in s m1. The E and T fluxes (W m2) are
converted to rates (mm d1) using a temperature-dependent
function of the latent heat of vaporization.
[22] The LAI can be used to partition the net radiation into
net radiation of the soil (Rn,soil) and the canopy (Rn,canopy)
[Shuttleworth and Wallace, 1985]. The increase in LAI
results in an exponential decrease in the fraction of radiation
available for the soil, and vice versa for the canopy. The
energy dissipation due to interception losses is subtracted
from the total net radiation. This energy is computed from
the actual interception evaporation rates and the latent heat
of vaporization being associated with that. The net radiation
at the soil and canopy can be calculated using Beer’s law as
follows:
Rn;soil ¼ fð1 o ÞR# Ln Igexp ðaLAIÞ;
Rn;canopy ¼ fð1 o ÞR# Ln Igf1 exp ðaLAIÞg;
(9)
(10)
where o is surface albedo (–); R; (Wm2) is the incoming
shortwave radiation; Ln, (Wm2) is the net longwave radiation; I is the interception of water by leaves expressed in
Wm2 ; and a is the light extinction coefficient for net radiation. The incoming shortwave radiation can be calculated
using daily measurements of shortwave transmissivity ( sw)
and the theoretical extraterrestrial radiation (Rtoa). The
parameterization for R; and Ln, is taken from the FAO Irrigation and Drainage Paper 56 [Allen et al., 1998]. The sum of
Rn,soil and Rn,canopy constitute total net radiation Rn, after
being corrected for interception losses.
[23] The surface resistances in equations (7) and (8)
describe the influence of the soil on evaporation or canopy
transpiration. The soil resistance (rsoil) is a function of the
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topsoil effective saturation (Setop ), estimated using equation
(1). A power function defines this relationship [e.g.,
Camillo and Gurney, 1986; Clapp and Hornberger, 1978;
Dolman, 1993; Wallace et al., 1986]:
rcanopy ¼
rs;min
LAIeff
1
St Sv Sr Sm
;
Sm ¼ Ksf Sesub sinð2Sesub Þ
;
2
(13)
where Ksf describes the ability of plants to extract soil
moisture under different moisture conditions. It ranges
from 1 for sensitive plants to 1.5 for moderately sensitive
plants to 3 for insensitive (tenacious) plants. The value
rs,min represents the resistance to transpiration from canopy
under ideal conditions (no moisture stress, enough sunshine
etc.). The resistance rs,min can have different values for the
different land use classes. The rs,min is defined for a single
layer of leaves, therefore effective leaf area index LAIeff,
which describes the actual transpiring leaf mass, was used
for integration from leaf to canopy. The leaf area integrates
the vaporization process from leaf to canopy scale. The following equation, as described by Mehrez et al. [1992] and
Allen et al. [2006], was used to infer LAIeff :
LAIeff ¼
LAI
:
0:3LAI þ 1:2
ra;canopy ¼
(14)
zobs
z0;soil
zobs
ln 0:1z
0;soil
k 2 uobs
ln
zobs d
z0;m
zobs d
ln 0:1z
0;m
k 2 uobs
(15)
;
;
(16)
where k is von Karman constant ¼ 0.41[-], uobs is the wind
speed at observation height [ms1], d is displacement
height [m], z0,soil is the soil surface roughness, z0,m is the
surface roughness. The land use map is used to prescribe
values for z0,m. Bare soil has been assigned with a value of
z0,soil being 0.001 m. Research is in progress to derive surface roughness from radar imagery, and it is expected that
backscatter coefficients can describe roughness in the near
future.
[27] The soil heat flux (G) for land surface is calculated
using a sine function as described by Allen et al. [1998].
The maximum value for G is recorded in May for northern
latitudes, which coincides with a phase of –/4. For southern latitudes the phase is –/4 þ .
pffiffiffi
2At;year ksin 2J
P 4
G¼
exp ðaLAIÞ;
zd
(12)
where St is temperature stress, and a function of minimum,
maximum and optimum temperatures, as defined by Jarvis
[1976]; Sv is vapor pressure stress induced due to persistent
vapor pressure deficit; Sr is radiation stress induced by the
lack of incoming shortwave radiation; and Sm is soil moisture stress originating from the root zone. Sm is defined
using a sinusoidal relationship with sub soil effective saturation (Sesub ) and tenacity factor (Ksf) defined in American
Society of Civil Engineers (ASCE) [1996] as:
ra;soil ¼
(11)
where b and c are soil resistance parameters, which can
vary with soil type and are taken here as 30 and –3, respectively. The coefficients b and c can be calibrated under
local conditions if more information is available, such as
monthly rainfall amounts and detailed soil maps.
[24] Canopy resistance describes the resistance of vapor
flow through the transpiring vegetation and is a function of
the minimum stomatal resistance rs,min (s m1), in association with a number of reduction factors and the leaf area.
[25] The canopy resistance under actual growing conditions can be computed using the common Jarvis-Stewart
parameterization [Jarvis, 1976; Stewart, 1988]. The JarvisStewart parameterization is common in many soil-vegetationtransfer models. It describes the joint response of soil moisture
and LAI on transpiration fluxes in a biophysically justified
manner. The Jarvis-Stewart parameterization describes the
response of stomata to environmental factors in the form of
minimal resistance multiplied by the product of interacting
stresses on plants, and is computed as follows:
[26] The aerodynamic resistance for soil (ra,soil) and
canopy (ra,canopy) can be computed [Allen et al., 1998;
Choudhury et al., 1986; Holtslag, 1984] as:
ln
rsoil ¼ bðSetop Þc ;
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(17)
where At,year is the yearly amplitude for air temperature ;
J is the Julian day measured in seconds; k is the soil thermal conductivity (W m1 K1), which has a linear relationship with topsoil moisture; a is the same light extinction
coefficient as used in Beers law, see equations (9) and
(10); zd (m) is the damping depth that is calculated as:
sffiffiffiffiffiffiffiffiffiffiffiffi
2kP
zd ¼
;
2cp
(18)
where P is the period in seconds ; and cp is the volumetric
heat capacity (a function of the porosity and Setop ). Equation
(17) includes light interception effects on soil heat flux.
3.4. Calibration and Validation Approaches
[28] The cloud optical depth measures the attenuation of
solar radiation passing through the atmosphere due to scattering and absorption by cloud droplets. The cloud optical
depth can be defined as the negative algorithm of the fraction of the incoming radiation that is not scattered or
absorbed in the atmosphere [Kitchin, 1987]. Maximum and
minimum threshold atmospheric transmissivity values were
taken into consideration to account for latitude, zenith
angle and diffuse radiation. The resulting atmospheric transmissivity ( MODIS) was checked and calibrated using the simplified—but doable—field methods suggested by Angstrom
[1924] and Hargreaves and Samani [1985]. Records of sunshine hours were used for the Angstrom equation. Sunshine
records were available from 24 stations in the study area.
The same 24 stations were used to get diurnal air temperature
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differences for the Hargreaves equation. Results from the
Angstrom and Hargreaves methods were used to determine a
linear fit through the origin for each time interval of 8 days,
to obtain calibrated shortwave transmissivity ( sw):
sw ¼ e: MODIS ;
(19)
where e is the regression coefficient.
[29] Minimum stomatal resistance values rs,min for each
LULC were used to fine-tune ETLook. The rs,min values
for agricultural classes were accepted to be between 40 to
140 s m1 [ASCE, 1996; Bastiaanssen and Bandara, 2001;
Radersma and de Ridder, 1996]. Various researchers [e.g.,
Allen et al., 1998; Monteith, 1981; Sharma, 1985; Vanderkimpen, 1991] suggested a value of rs,min ¼ 100 s m1 for
various agricultural crops like wheat, rice, beans, etc. First,
this default rs,min value was used for all agricultural classes
(n ¼ 11). The following values were assigned to the remaining classes: pastures 125 s m1, savannas 150 s m1, forests
150 and 300 s m1(for broadleaf and needleleaf forests,
respectively), sparse vegetation 200 s m1, and urban and
industrial settlements 60 s m1. Water bodies were
assigned 0 s m1 because water vapor molecules can be
transported into the atmosphere without physical barriers.
During the second run of ETLook, all LULC classes with
irrigated crops were assigned 80 s m1 and the rainfed
crops were assigned a value of 150 s m1. During the third
run, adjustments were made to the urban and industrial settlements land use, and a rs,min value of 500 s m1 was
assigned.
[30] The ET output data cannot be used for water resources management without testing its accuracy. Results from
previous studies based on soil moisture and lysimeter
experiments were used for validation. Pakistan Agricultural
Research Council (PARC) measured actual ET at Peshawar, Bhalwal, Faisalabad, Bhakkar, MianChannu, and Tandojam representing upper, middle and lower parts of the
basin [PARC, 1982]. The ET results of PARC are for the
years 1975–80 following an internationally funded study.
Data collection discontinued when the project ended, yet
it seems to be one of the most basic databases in Pakistan.
More recent field measurement study was conducted by
Ahmad [2002] at the Soil Salinity Research Institute,
Pindi Bhattian (31 520 34.200 N, 73 200 50.200 E) and Ayub
Agricultural Research Institute, Faisalabad (31 230 26.200 N,
73 020 49.800 E). As part of a field investigation program
during 2000 and 2001, he measured actual ET in rice/
wheat and cotton/wheat systems by a temporarily installed
Bowen ratio energy balance system.
[31] ETLook estimates were also checked against previously conducted remote sensing and modeling studies. The
ET estimates provided by Bastiaanssen et al. [1999] for the
Sirsa irrigation circle in India were checked. Other studies
[e.g., Shakoor et al., 2006; Sarwar and Bill, 2007; Ahmad
et al., 2009; Shakir et al., 2010] determined ET in selected
areas within the basin for different years. Previous studies
were synthesized and used to compare with ETLook estimates. The coefficient of determination (R2), Root Mean
Square Error (RMSE) and Relative Error (RE) were calculated to estimate the difference of the ETLook estimates
with the previous studies.
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3.5. Sensitivity and Uncertainty Analysis
[32] A sensitivity analysis was performed to check the
contribution of selected main input parameters to the output
results. The sensitivity of ET was tested for a number of
input parameters, i.e., AMSRE, NDVI, rs,min, rsoil and sat.
Annual mean climatic conditions were assumed for the analysis. One factor at a time methodology was adopted to check
the variance in the outputs due to input variability [e.g.,
Pitman, 1994]. The analysis was conducted on two representative land uses, i.e., ‘‘bare soil’’ and ‘‘irrigated rice-wheat
rotation’’ at locations 71 220 54.12300 E, 28 380 50.04200 N and
75 230 53.5900 E, 30 400 37.71900 N, respectively. Randomly
generated uniform distribution of AMSR-E based soil moisture values (n ¼ 1000) were used while keeping other parameters constant to check the variations in E, T and ET.
The analysis was performed using representative NDVI values of 0.05 for bare soil and 0.67 for irrigated land use. A
complete sensitivity analysis representing the change in the
response variable caused by a unit change of an explanatory
variable, while holding the rest of parameters constant, was
performed. A Sensitivity Coefficient (SC ¼ out/in) was
then calculated for each input parameter as described by Gu
and Li. [2002]. The sensitivity coefficient was normalized
by the mean values representing the range of each pair of
output and input variable. This normalized sensitivity coefficient is called Sensitivity Index (SI) and can be positive or
negative. SI makes it feasible to compare the results of different input parameters. A higher absolute value indicates
higher sensitivity. A negative SI indicates an inverse relationship between input parameter and response variable. SI
can be represented as:
SI ¼ ðMin =Mout Þðout =in Þ;
(20)
where Min and Mout are the mean values of the input and
output range, respectively.
[33] In addition, a stochastic uncertainty analysis was performed. A Monte Carlo simulation experiment using 1000
pairs of randomly generated input parameters was performed
to investigate the model uncertainty. The values of the sensitive parameters were varied, while other climatic variables
were kept constant.
4.
Results
4.1. Surface Energy Balance
[34] The temporal variation of each component of the surface energy balance of the Indus Basin for the hydrological
year 2007 is presented in Figure 2. The values represent the
spatial averages for the whole Indus Basin. The average values attained by the surface energy fluxes with their standard
deviations (SD) are provided in Table 1. A high variability
from the mean is observed for the year, especially for net
shortwave radiation (R;), net radiation (Rn) and sensible heat
flux (H). The large variation in climate during summer and
winter is the probable cause of the high SD.
[35] Rn is the dominant source of energy for land surface
processes. The annual average value for Rn was 112.3
Wm2 with a standard deviation of 46.7 Wm2. The lower
Rn values (<80 Wm2) prevailed during the winter season
(DOY 305–361 and 1–65). This low Rn is probably due to
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Figure 2. Temporal variation of components of the surface energy equation during 2007 in the entire
Indus Basin (116.2 mha). The dashed lines represent 24 days moving average values.
the lower net shortwave radiation. After DOY 66, the Rn
continuously increased to a maximum of 177.4 Wm2 for
an 8 day period (DOY 161). In the summer season the maximum Rn (>150 Wm2) was observed during DOY 113–
225 while the average was 163.5 Wm2. For the same period, Rn values fluctuated considerably with sudden depressions during DOY 145–225, corresponding to the monsoon
season with clouds. Afterward, the net radiation decreased
gradually and reached its minimum values again in winter.
[36] The dry arid environment of the Indus Basin (annual
rainfall is 383 mm) causes the net radiation to dissipate
mainly into sensible heat flux (H). H followed the same
temporal pattern as that of the net radiation and the daily
mean value varied between a minimum of 37.2 and maximum of 131.6 Wm2. The average annual value for a 24 h
period of H was 79.5 Wm2. When the soil is moist, a significant part of the energy is dissipated into evaporation.
E showed two peaks during its annual cycle (Figure 2).
The seasonal peaks for the entire Indus Basin correspond to
the two agricultural seasons, once in rabi and once in
kharif. The E varied in the range from 10.9 (during winter
with more cloud covers and lower temperatures) to 57 Wm2
(during the periods of more canopy cover and higher temperature), with an annual average of 32.4 Wm2. The average
E for the entire Indus Basin coincided with an ET of
1.2 mm d1, but large variability among LULC classes
occurred. The basin-wide evaporative fraction () is calculated as 0.28, equivalent to a Bowen ratio of 2.5. Hence, the
amount of sensible heat released into the atmosphere is 2.5
times more than for water vapor, if both are expressed in
energy terms.
[37] Soil heat flux (G) is normally ignored when seasonal
averages are considered because of its small scale. However, G can account for a significant portion (3 – 5%) of the
total energy during summer (DOY 113–171) indicating that
G is transferred from shallow to deep soil while for the rest
of the year, the reverse process occurs.
Table 1. Minimum, Maximum, and Average Values of Surface
Energy Fluxes in the Indus Basin Attained During the Year 2007a
4.2. Actual Evapotranspiration Estimates
[38] The total transpiration and evaporation in the basin
was estimated at 233 km3 yr1 and 263 km3 yr1, respectively. The major portion of water was consumed as nonbeneficial evaporation (E), mainly from water-logged soils,
dry soils and open water bodies. High annual ET values
occurred on the alluvial plains as depicted in Figure 3. Irrigated agriculture is the major land use class (22.6%) in the
basin and is a major consumer of water. It accounts for the
annual ET rates of between 700 and 1200 mm and represents
the middle part of the frequency distribution in Figure 4.
The highest values (1200–1550 mm yr1) were found in the
tail end of the basin: in particular in the right bank of the
Fluxes
Minimum Maximum
2
Net shortwave radiation (Wm )
Net longwave radiation (Wm2)
Net radiation (Wm2)
Soil heat flux (Wm2)
Sensible heat flux (Wm2)
Latent heat flux (Wm2)
Evapotranspiration (mm d1)
Evaporative fraction (¼)
95.70
–75.60
46.20
–7.10
37.20
10.90
0.39
0.19
237.50
–36.90
177.40
8.10
131.60
57.00
2.10
0.36
Mean
SD
170.10
–57.80
112.30
0.34
79.50
32.40
1.20
0.28
45.10
10.80
46.70
5.20
29.80
14.30
0.50
0.05
a
The entire basin is covered and the values represent average flux densities for periods of eight days including daytime and nighttime.
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Figure 3. ETLook estimated cumulative actual evapotranspiration for the hydrological year 2007
(January to December). The canal command areas for irrigated cropland are superimposed on the ET map.
Indus River, and southern parts toward the Indian Ocean, in
the Sindh Province of Pakistan. Water-logged soils, rice
paddies with shallow phreatic surfaces, and flooded areas
normally occur in these parts of the basin, especially during
kharif. Besides higher soil water content, factors such as
higher solar radiation, higher air temperatures, more rainfall,
and cultivation of higher consumptive use crops are the reasons for the higher ET.
[39] Figure 4 provides the frequency distribution of annual ET. The average ET for all land use classes was
Figure 4. Frequency distribution of the ETLook estimated annual ET in the Indus Basin at spatial resolution of 1 km 1km for 2007.
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426 mm yr1 during 2007. The 2% lowest value was
60 mm yr1 and the 2% highest value was 1550 mm yr1.
[40] A sensitivity analysis was performed to understand
the role of topsoil moisture data in the ET estimation procedure. The results are provided in Figures 5a and 5b. Two
land use classes and the average climatological condition
of the year were used.
[41] The curves in the Figures 5a and 5b display the
response of model outputs (E and T) to variation in surface
soil moisture. It is evident from the figure that the ET
Figure 5. The response of evaporation, transpiration and
evapotranspiration rates to surface soil moisture (n ¼ 100) for
two representative land uses (a) Bare soil (71 220 54.12300 E,
28 380 50.04200 N) and (b) Irrigated rice-wheat rotation
(75 230 53.5900 E, 30 400 37.71900 N). NDVI values of 0.05 and
0.67 were selected for bare soil and full grown irrigated ricewheat land use, respectively.
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responds to surface soil moisture variability. Bare soil shows
a fast response in E to surface soil moisture, while T remains
negligibly small by absence of leaves (NDVI ¼ 0.05). E is
the dominant flux in the overall ET process of bare soil. The
response is curvilinear with the highest sensitivity occurring
between 0.05 and 0.25 cm3 cm3. The effects are lower
when > 0.25 cm3 cm3 prevails. The parameter rsoil and
the nonlinearity of equation (11) is one reason for this result.
Another explanation is the nonlinear relationship between
the resistance and the latent heat fluxes that generally exists
(not shown in this paper). The combined effect yields the
S-type curve that is portrayed in Figure 5a, and to a lesser
extent in Figure 5b.
[42] Figure 5b reveals that, in closed canopies, T dominates E. The net radiation is absorbed partially by the canopy, and the bare soil surface receives less energy for
evaporation. At an NDVI of 0.67, E increases with increasing topsoil moisture, up to 0.18 cm3 cm3. Apparently
there is always soil evaporation in rice-wheat rotation systems, which is confirmed by many other agrohydrological
studies [e.g., Ahmad et al., 2002; Sarwar and Bastiaanssen,
2001]. Canopy transpiration depends entirely upon root
zone soil moisture rather than on the surface soil moisture.
This is correct as crop can transpire intensely while the topsoil is dry. This is in fact promoted by introducing drip irrigation systems. Therefore, T shows less sensitivity to
surface soil moisture. The same can be concluded on the
total ET response to surface soil moisture changes.
[43] The effect of other input parameters on ET is summarized in Table 2. The lower and higher ranges of model
input parameters are given, together with ET estimates for
the average climate in the Indus Basin. The values of the
input parameters were changed with specific increments.
The sensitivity index (SI) was determined and the parameters were ranked based on the absolute values. The surface
soil moisture appears to be the most important parameter
for describing ET variability, with ET values ranging from
2.3 to 6.3 mm d1, followed by the coefficient c in rsoil
with a range of 2.5 to 6.2 mm d1. The measurements of
AMSR-E are thus essential for achieving proper ET modeling results, and form the key input parameter of ETLook as
was suggested in the introduction.
[44] Model parameter sensitivity was investigated using
a Monte Carlo simulation experiment with 1000 pairs of
randomly generated input parameters. Based on this experiment the mean ET for ‘‘irrigated rice-wheat rotation’’ was
3.2 mm d1 with an SD of 1.7 mm. The standard error for
this distribution was 0.05 mm. A 95% confidence interval
was used to determine the 2.5th and 97.5th percentiles,
which ranged between 3.1 and 3.3 mm d1. This level of
uncertainty reflects that the model generates results with a
potential error of 3.4%.
4.3. Validation
4.3.1. Field Measurements
[45] Several field methods to measure ET fluxes can be
used to validate the results. AsiaFlux has erected flux towers in China and India, but not in Pakistan [Mizoguchi
et al., 2009]. Therefore, to evaluate performance, ET estimates by ETLook were compared with the measured values
given by PARC [1982] and Ahmad [2002] for 1975–1980
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Table 2. Sensitivity of Estimates of ET to Model Parameter Values for Irrigated Rice-Wheat Land Usea
Resulted ET (mm)
Input Value
Parameter
3
3
AMSRE (cm cm )
rsoil, c (s m1)
rs,min (s m1)
NDVI (-)
rsoil, b (s m1)
Min
Base
b
Max
in
Min
Min
Base
Max
out
Mout
SC
SI
0.05
10.0
40.0
0.05
10.0
0.15
3.0
80.0
0.45
30.0
0.35
5.0
500
0.67
70.0
0.30
15
460
0.62
60
0.2
7.5
270
0.36
40
2.3
2.5
5.6
3.9
5.8
5.1
5.1
5.1
5.1
5.1
6.3
6.2
3.4
5.9
4.3
4.0
3.7
2.2
2.0
1.5
4.3
4.3
4.5
4.9
5.1
13.3
0.23
0.005
3.2
0.025
0.62
0.40
0.3
0.24
0.2
a
The last column depicts the sensitivity in terms of slope. is change, and M is mean.
Definition of fixed reference values during sensitivity test.
b
and 2000–2001, respectively (onward referred to as ‘‘measured’’ values). Figure 6 shows the results of irrigated crops.
[46] The correlations were good with an R2 of 0.70, and
an RMSE of 163 mm (0.45 mm d1). The RE between
ETLook and measured ET values ranged from –1.9% to
–28% with an average of –11.5%. The negative RE means
ET figures from ETLook were lower than the field measurements. The regression line fitted through the origin has a
slope of 0.89. This implies that ETLook estimates for 2007
were 11% lower than ET from previous studies. This difference of 11% is acceptable, considering the climatic differences between the years, the scale difference between in
situ measurements, and the 1 km remote sensing pixel size,
as well as the uncertainty embedded in field measurements.
[47] Figure 7 shows the comparison of annual and seasonal ET from ETLook, from previous remote sensing and
modeling studies (year 1995–96, 2001–02), and from other
models (year 2000, 1999–2006) (onward referred to as
‘‘modeled’’ values). There is a reasonably good agreement
between ETLook estimates and modeled values at annual
scale, with an R2 of 0.76 and an RMSE of 108 mm yr1 (or
0.29 mm d1). The values for the rabi season are reasonable (R2 of 0.60 and RMSE of 47.9 mm). However, the
kharif season shows a relatively low R2 (0.54) and a high
RMSE of 70.7 mm (or 0.39 mm d1). Note that there is no
bias toward the lower or higher end of the ET data, and that
Figure 6. A comparison of evapotranspiration in ricewheat rotation measured by previous studies, and those
estimated by ETLook for 2007 in the Indus Basin.
the average slope is 1.05. Since Figure 6 suggests an underestimation of ET, and Figure 7 an overestimation, we
believe that the ETLook estimates of ET are within a plausible and acceptable range for this type of vast basins with
scarce data.
[48] ETLook has also been validated in regions other
than the Indus, e.g., Australia and China. Some of these
unpublished results are presented as a demonstration of the
model performance under different climates and landscapes
of ETLook. The National Water Commission of the Australian Government has provided Australian Water Resources
(AWR) data for the year 2005.The water use data of eight
states and 23 jurisdictional areas are publically available
through http://www.water.gov.au/. The ET is computed as
the difference between rainfall and runoff; storage changes
and groundwater are not considered. ET values from the
water balance were compared against ETLook (Figure 8).
Considering that the annual values were averaged over a
large area, correlation was reasonable with an R2 of 0.70
and an RMSE of 112 mm (0.31 mm d1). The RE between
the ETLook and AWR ET values ranged from –40% to
36% with an average of –2.8%.
Figure 7. Comparison of evapotranspiration modeled/
estimated by previous studies conducted during the years
1995–1996 [Bastiaanssen et al., 1999], 2000 [Sarwar and
Bill, 2007], 2001–2002 [Ahmad et al., 2009; Shakoor et al.,
2006] and 1999–2006 [Shakir et al., 2010] and ET estimated by ETLook for 2007 in the Indus Basin.
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Figure 8. Comparison of evapotranspiration estimated by
ETLook and estimates provided by Australian water commission for the year 2005.
[49] In China, ETLook estimated latent heat flux in the
year 2009 was compared with flux tower measurements
obtained from the eddy covariance flux measurement station
at Heibei, Qinghai, China (37 360 N, 101 200 E) (Figure 9).
Annual values correlated well with an R2 of 0.92 and an
RMSE of 11 mm (0.04 mm d1). The RE of 9.5% between
the two datasets shows that the ETLook estimated ET falls
within the range of the ground measurements considering
the mismatch of scales between 1 km ETLook pixel estimates and ground measurements (flux tower).
4.3.2. Water Balance
[50] A map depicting differences in rainfall and evapotranspiration (R–ET) was prepared using TRMM rainfall
Figure 9. Comparison of latent heat flux estimated by
ETLook and measured by flux tower at Heibei, Qinghai,
China (37 360 N, 101 200 E) for the year 2009. Each point
represents 8-day average value.
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data, calibrated by Cheema and Bastiaanssen [2012], and
the ET results from this study (Figure 10). It shows areas
with net water production (R > ET) and areas with net
water consumption (ET > R). This indicates the value of
spatial data to describe hydrological processes and withdrawals. The pixels that produce water (R > ET) are discharge areas responsible for streamflow and groundwater
recharge. These areas are in the upstream parts of the basin,
and are the source of the rivers Indus, Jhelum and Sutlej
that feed the large reservoirs Tarbela, Mangla and Bhakra,
respectively. Areas with sparse vegetation and low ET also
have higher rainfall than ET and are water producing areas.
Large parts of the Tibetian Plateau comprise such areas.
The Rajasthan Desert between India and Pakistan also
exhibits positive values of R–ET, which suggests groundwater recharge.
[51] Net water consumption areas are generally the irrigated areas, lakes and reservoirs. Irrigation increases crop
ET far beyond the level of rainfed crops. In the Indus Basin,
30.3% of the total land area is composed of net consumer
areas, and 22.6% (26.02 mha) is irrigated land. The mountain valleys are net water consumers; the valleys receive
both seepage water through the groundwater system and surface water from the higher elevated mountains, which generally results in shallow water table areas in the vicinity of
streams.
[52] The water balance of the irrigated areas covering
26.02 mha was computed to validate ET results on a large
scale (Table 3). Total annual groundwater abstraction in
Pakistan’s part of the Indus Basin is given by Qureshi et al.
[2010] as 51 km3. Chadha [2008] estimated that for the Indian part of the Indus Basin 18.5 km3 is being abstracted
from the groundwater system. This totals to 69.5 km3 yr1.
The surface water releases into the main canals add up to
122 and 36 km3 yr1 in Pakistan and India, respectively.
These data on releases from Tarbela, Mangla, Chashma,
Thein, Pong and Bhakra reservoirs, as well as flows into
the main irrigation canals were obtained from Punjab Irrigation Department and Indus Water Commission, Pakistan.
If we assume a conveyance efficiency of 80% that is locally
checked and verified [Habib, 2004; Jeevandas et al.,
2008], then 126.4 km3 yr1 will arrive at the farm gate
through the network of canals. Adding the 69.5 km3
[Chadha, 2008; Qureshi et al., 2010] of groundwater from
locally operating tubewells the total amount of water used
is about 196 km3. If we take an on-farm irrigation efficiency of 80% to describe losses of water that is not properly stored in the root zone, the total ET from irrigation
will be 156.8 km3 yr1. Note that a regional scale on-farm
efficiency for the total irrigation system includes recycling
of nonconsumed irrigation water [Perry, 2007]. A total irrigation efficiency of 64% (0.8 0.8) for one contiguously
irrigated alluvial plain can be considered realistic [Habib,
2004; Seckler et al., 1999]. It can however also be 60% or
70%. The rainfall over the irrigated area is 117 km3 yr1.
The net rainfall infiltrated into the soil—after runoff and
percolation losses—and available for uptake by roots is 94
km3 yr1 (assuming 80% efficiency). The total ET for the
irrigated land on the basis of water balance is 94 þ 156.8 ¼
250.8 km3 yr1, or 964 mm yr1. ETLook results provided
an estimate on the basis of the energy balance as being
254 km3 yr1, or 974 mm yr1. Without any further data at
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Figure 10.
year 2007.
Rainfall-Evapotranspiration (R–ET) difference map of the Indus Basin for the hydrological
hand, we conclude that the results are congruent and within
acceptable ranges that are usually related to water balances
of irrigated areas.
5.
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Limitations
[53] The power of having access to daily soil moisture
data from passive microwave measurements onboard satellites is at the same time a limitation by the low resolution
of AMSR-E surface soil moisture pixels (25 km). Several
methods exist to deal with the downscaling of soil moisture, but the best method that is doable under a wide range
of conditions still needs to be found. More sophisticated
solutions on downscaling can be gleaned from topographic
information (e.g., height above drain, distance to drain,
accumulated upstream drainage area) and soil properties
(infiltration capacity, water holding capacity, drainage
capacity). It is expected that satellites with synthetic aperture radar will provide high-resolution soil moisture values
in an operational context in the near future, in addition to
the thermal data. The analytical relationships between topsoil and subsoil moisture need improvement and more testing under different environmental conditions. For this
reason we refer to this version of ETLook as 1.0. An
improved parameterization of Sesub using various combinations of climate, soil types, drainage conditions and LAI is
under development. A more complex solution for equation
(2) will however not necessarily improve the performance
of ETLook.
[54] The soil moisture estimated by AMSR-E does have
limitation to use, but availability of new data sources on
soil moisture will improve the situation. New sophisticated
satellites e.g., Soil Moisture and Ocean Salinity (SMOS)
and Advanced SCATterometer (ASCAT) can be best alternatives of AMSR-E. With the discontinuity of AMSR-E
and SMOS data, we believe that the ASCAT data is a good
replacement of AMSR-E, and it provides an operational
data flow of day-to-day variability of moisture conditions.
[55] This analysis was conducted for a one year cycle
only, to raise confidence in using the first version of
ETLook algorithm (ETlook 1.0). Future analysis with longer time series is recommended, since shorter time series
may be of low significance. Despite the limitations mentioned, the current paper has demonstrated that the ET
results show potential for determining water depletion in
ungauged basins, and that the results are congruent with the
other sources of ET data.
6.
Summary and Conclusions
[56] The first requirement for an operational ET monitoring system is that the satellite data must be available at all
Table 3. Water Balance for the Irrigated Areas in the Indus Basin During the Hydrological Year 2007
Irrigation (IRR)
3
km
mm
Evapotranspiration (ET)
Annual
Rainfall R
From Surface
Water
At Farm
Gate
From Ground
Water
Total
(Column 4 þ 5)
IRR
R
Total
ETETLook
117
451
158
607
126.4
486
69.5
267
196
753
156.8
603
94
361
250.8
964
254
974
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BASTIAANSSEN ET AL.: ENERGY BALANCE AND ET OF THE INDUS BASIN
times. Microwave satellite data are operationally provided—
even under all weather conditions—and their growing
number of standard databases form an attractive source for
developing ET models. ETLook can assess the spatial and
temporal (daily, 8-day, or monthly) patterns of the surface
energy balance and actual evapotranspiration. Computing
E and T separately, on the basis of the energy balance, has
the advantage that complex transient moisture flow computations in the unsaturated topsoil can be circumvented. The
novelty of this paper is a doable computational method for
nonbeneficial E and beneficial T that can be applied under
conditions of persistent overcast skies, and in data scarce
environments. The sensitivity analysis revealed that the surface soil moisture is the most important parameter for
describing ET variability. Variability of surface soil moisture
revealed that the ET values for rice-wheat rotation system
on an average day ranged between 2.3 to 6.3 mm d1,
followed by the coefficient c of soil resistance, with a range
of 2.5 to 6.2 mm d1.
[57] Good agreement was attained between ETLook and
previously conducted field measurements and remote sensing studies. R2 varied between 0.70 and 0.76 at annual time
scale (RMSE: 0.45 and 0.29 mm d1, respectively). Tests
in Australia and China provided similar agreements based
on watershed measurements. The water balance of 26.02
mha of irrigated land is congruent and matches generic
data on surface water and groundwater supply. There are
discrepancies in timescales shorter than a year. However,
no bias was evident toward the lower or higher end of the
ET values. The observed errors could be due to the meteorological differences between the years of study. The determination of wind speed and air humidity needs more
attention in future studies. Better quality soil maps will also
improve the quality of the ET results.
[58] The average value for latent heat flux in the Indus
Basin is 32 Wm2, which corresponds with an ET of
1.2 mm d1 (426 6 14.5 mm yr1). The average value for
rainfall is 383 mm yr1. Over-exploitation and negative
storage changes of water occur at the basin scale (ET > R).
The negative change in storage can be ascribed to reduced
volumes of water stored in reservoirs and aquifers. Retirement of glaciers also contribute to water storage changes.
[59] Acknowledgments. The authors are thankful to the Higher Education Commission (HEC) of Pakistan for providing funds to carry out this
research. Thanks also go to the Pakistan Meteorological Department, Punjab Irrigation Department, and Indus Water Commission, in Pakistan, for
providing required data. Peter Droogers of FutureWater, kindly provided
the soil physical properties required for the soil moisture downscaling. The
authors are also thankful to Ms. Annemarie Klaasse (Water Watch) for
providing necessary support in collecting satellite data. We thank the anonymous reviewers for their helpful comments to improve the quality of the
manuscript.
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